A real-time dashboard for managing pathology processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: The Eastern Ontario Regional Laboratory Association (EORLA) is a newly established association of all the laboratory and pathology departments of Eastern Ontario that currently includes facilities from eight hospitals. All surgical specimens for EORLA are processed in one central location, the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital (TOH), where the rapid growth and influx of surgical and cytology specimens has created many challenges in ensuring the timely processing of cases and reports. Although the entire process is maintained and tracked in a clinical information system, this system lacks pre-emptive warnings that can help management address issues as they arise. AIMS: Dashboard technology provides automated, real-time visual clues that could be used to alert management when a case or specimen is not being processed within predefined time frames. We describe the development of a dashboard helping pathology clinical management to make informed decisions on specimen allocation and tracking. METHODS: The dashboard was designed and developed in two phases, following a prototyping approach. The first prototype of the dashboard helped monitor and manage pathology processes at the DPLM. RESULTS: The use of this dashboard helped to uncover operational inefficiencies and contributed to an improvement of turn-around time within The Ottawa Hospital's DPML. It also allowed the discovery of additional requirements, leading to a second prototype that provides finer-grained, real-time information about individual cases and specimens. CONCLUSION: We successfully developed a dashboard that enables managers to address delays and bottlenecks in specimen allocation and tracking. This support ensures that pathology reports are provided within time frame standards required for high-quality patient care. Given the importance of rapid diagnostics for a number of diseases, the use of real-time dashboards within pathology departments could contribute to improving the quality of patient care beyond EORLA's.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it